00-Crash-Course-note.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TEST tensor flow library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hello = tf.constant(\"hello world\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'hello world'\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(hello))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = \"string\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function str.capitalize>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.capitalize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 4 - Crash Course Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_list = [1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.array(my_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr = np.array(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   1.22222222,   2.44444444,   3.66666667,\n",
       "         4.88888889,   6.11111111,   7.33333333,   8.55555556,\n",
       "         9.77777778,  11.        ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,11,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
       "        11.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,11,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[55, 69, 32],\n",
       "       [ 2, 26, 18],\n",
       "       [33, 33, 22]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0,100, (3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([95, 11, 81, 70, 63, 87, 75,  9, 77, 40])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(101) # To fix the same result, you have to run the seed every time\n",
    "np.random.randint(0,100,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr = np.random.randint(0,100,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 63, 40, 60, 92, 64,  5, 12, 93, 40])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.299999999999997"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 63, 40, 60, 92, 64,  5, 12, 93, 40])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4, 63, 40, 60, 92],\n",
       "       [64,  5, 12, 93, 40]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(2,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mat = np.arange(0,100).reshape(10,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
       "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
       "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
       "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [10, 11, 12],\n",
       "       [20, 21, 22]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[0:3,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_filter = mat >50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[my_filter]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/yuchenlu/SelfLearning/Tensorflow-Bootcamp-master'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('00-Crash-Course-Basics/salaries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>50000</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>120000</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alyssa</td>\n",
       "      <td>80000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Salary  Age\n",
       "0    John   50000   34\n",
       "1   Sally  120000   45\n",
       "2  Alyssa   80000   27"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50000</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000</td>\n",
       "      <td>Sally</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80000</td>\n",
       "      <td>Alyssa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Salary    Name\n",
       "0   50000    John\n",
       "1  120000   Sally\n",
       "2   80000  Alyssa"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Salary', 'Name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Salary'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>83333.333333</td>\n",
       "      <td>35.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>35118.845843</td>\n",
       "      <td>9.073772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>65000.000000</td>\n",
       "      <td>30.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>80000.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>39.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>120000.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Salary        Age\n",
       "count       3.000000   3.000000\n",
       "mean    83333.333333  35.333333\n",
       "std     35118.845843   9.073772\n",
       "min     50000.000000  27.000000\n",
       "25%     65000.000000  30.500000\n",
       "50%     80000.000000  34.000000\n",
       "75%    100000.000000  39.500000\n",
       "max    120000.000000  45.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "myfilter = df['Salary'] > 60000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>120000</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alyssa</td>\n",
       "      <td>80000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Salary  Age\n",
       "1   Sally  120000   45\n",
       "2  Alyssa   80000   27"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[myfilter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['John', 50000, 34],\n",
       "       ['Sally', 120000, 45],\n",
       "       ['Alyssa', 80000, 27]], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to show the graph and plot on jupyter notebook\n",
    "%matplotlib inline \n",
    "\n",
    "# if the code is not write on jupyter notebook, \n",
    "# write plt.show() to display the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.arange(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x117a351d0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pFCJ1eeUVeOaZcM/CDjvETiPSYLUNPt9nZk8SlsEoNbNJQEXa8WtykE8kf+y2G8yYAVtt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\n5gv/loCtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10471f240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,\"r--\")\n",
    "plt.xlim(0,4)\n",
    "plt.ylim(0,10)\n",
    "plt.title(\"TITLE\")\n",
    "plt.xlabel(\"X LABEL\")\n",
    "plt.ylabel(\"Y LABEL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
       "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
       "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
       "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat = np.arange(0,100).reshape(10,10)\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1180fe358>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naszApcYMXGrMwKXGDFxq7H8BCw9aR7jYPtMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180a6a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(mat,cmap=\"seismic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1182a7f98>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IaIF21KkUqoguy6lfRDSbgZz6RUTjtaNO9X+le0RMX3K5ZdIY0kJJ35a0SdJGSSuL7cdL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\nZz/Z6++/qQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11831e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = np.random.randint(0,1000, (10,10))\n",
    "plt.imshow(mat)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>50000</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>120000</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alyssa</td>\n",
       "      <td>80000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Salary  Age\n",
       "0    John   50000   34\n",
       "1   Sally  120000   45\n",
       "2  Alyssa   80000   27"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('00-Crash-Course-Basics/salaries.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1184a4a20>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JaqWzwEgyL8m3k9yV5O4k723aX5jk1iT3N98LJhm/PMl9STYmuaSrOiVJ7XS5h7EdOKOq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\nUisGhiSpFQNDktSKgSFJauX/A0T+lkUQSosFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118473518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x=\"Salary\", y =\"Age\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SciKit-Learn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.randint(0,100,(10,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[87, 46],\n",
       "       [52, 16],\n",
       "       [70, 71],\n",
       "       [84, 10],\n",
       "       [62, 96],\n",
       "       [57, 23],\n",
       "       [86, 85],\n",
       "       [26, 76],\n",
       "       [66, 54],\n",
       "       [17, 65]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler_model = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.preprocessing.data.MinMaxScaler"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(scaler_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yuchenlu/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/sklearn/utils/validation.py:444: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MinMaxScaler(copy=True, feature_range=(0, 1))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler_model.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.41860465],\n",
       "       [ 0.5       ,  0.06976744],\n",
       "       [ 0.75714286,  0.70930233],\n",
       "       [ 0.95714286,  0.        ],\n",
       "       [ 0.64285714,  1.        ],\n",
       "       [ 0.57142857,  0.15116279],\n",
       "       [ 0.98571429,  0.87209302],\n",
       "       [ 0.12857143,  0.76744186],\n",
       "       [ 0.7       ,  0.51162791],\n",
       "       [ 0.        ,  0.63953488]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler_model.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yuchenlu/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/sklearn/utils/validation.py:444: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.41860465],\n",
       "       [ 0.5       ,  0.06976744],\n",
       "       [ 0.75714286,  0.70930233],\n",
       "       [ 0.95714286,  0.        ],\n",
       "       [ 0.64285714,  1.        ],\n",
       "       [ 0.57142857,  0.15116279],\n",
       "       [ 0.98571429,  0.87209302],\n",
       "       [ 0.12857143,  0.76744186],\n",
       "       [ 0.7       ,  0.51162791],\n",
       "       [ 0.        ,  0.63953488]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler_model.fit_transform(data) #transform and fit at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[49, 74, 82, 37],\n",
       "       [53, 93, 73,  5],\n",
       "       [37, 56, 35, 15],\n",
       "       [40, 42, 71, 37],\n",
       "       [46, 71, 91, 97],\n",
       "       [36, 43, 27, 66],\n",
       "       [57,  3, 55, 70],\n",
       "       [41, 67, 72, 88],\n",
       "       [67, 75, 69, 92],\n",
       "       [20, 15, 57,  9],\n",
       "       [69, 97, 91, 51],\n",
       "       [54, 45, 38, 15],\n",
       "       [42, 61,  8,  3],\n",
       "       [88, 72, 41, 24],\n",
       "       [93, 28, 95, 43],\n",
       "       [48, 69, 42, 66],\n",
       "       [82, 66, 88, 78],\n",
       "       [14, 53, 46, 14],\n",
       "       [27, 68, 21, 38],\n",
       "       [42, 30, 88, 79],\n",
       "       [56, 65, 93, 37],\n",
       "       [87, 66, 16, 60],\n",
       "       [25, 99, 30, 41],\n",
       "       [72, 45, 26, 87],\n",
       "       [65, 60, 89, 70],\n",
       "       [33, 59, 28, 16],\n",
       "       [49, 95, 41, 11],\n",
       "       [86, 15, 95, 91],\n",
       "       [19, 69,  1, 77],\n",
       "       [63, 87, 28, 70],\n",
       "       [ 5,  3, 12, 62],\n",
       "       [26, 96, 61, 87],\n",
       "       [74, 99, 97, 64],\n",
       "       [95,  3, 88, 59],\n",
       "       [38, 29, 31, 88],\n",
       "       [57,  9, 62, 31],\n",
       "       [13, 35, 47,  2],\n",
       "       [17, 50, 22, 30],\n",
       "       [36, 22, 97, 51],\n",
       "       [38, 28,  4,  0],\n",
       "       [12, 90,  2,  7],\n",
       "       [44, 54, 49, 76],\n",
       "       [98,  3, 32, 91],\n",
       "       [43,  7, 42, 66],\n",
       "       [ 9,  5, 26, 66],\n",
       "       [22, 47, 27, 46],\n",
       "       [14, 52, 74, 54],\n",
       "       [ 3, 94, 36, 63],\n",
       "       [41, 18, 37, 45],\n",
       "       [82, 47, 66, 97]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata = np.random.randint(0,101,(50,4))\n",
    "mydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>f3</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>74</td>\n",
       "      <td>82</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>93</td>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "      <td>71</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>71</td>\n",
       "      <td>91</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36</td>\n",
       "      <td>43</td>\n",
       "      <td>27</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>67</td>\n",
       "      <td>75</td>\n",
       "      <td>69</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>57</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>69</td>\n",
       "      <td>97</td>\n",
       "      <td>91</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>54</td>\n",
       "      <td>45</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>42</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>88</td>\n",
       "      <td>72</td>\n",
       "      <td>41</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>93</td>\n",
       "      <td>28</td>\n",
       "      <td>95</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>48</td>\n",
       "      <td>69</td>\n",
       "      <td>42</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>82</td>\n",
       "      <td>66</td>\n",
       "      <td>88</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>46</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>56</td>\n",
       "      <td>65</td>\n",
       "      <td>93</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>87</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25</td>\n",
       "      <td>99</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>72</td>\n",
       "      <td>45</td>\n",
       "      <td>26</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>65</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>33</td>\n",
       "      <td>59</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>49</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>86</td>\n",
       "      <td>15</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>63</td>\n",
       "      <td>87</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>26</td>\n",
       "      <td>96</td>\n",
       "      <td>61</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>74</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>95</td>\n",
       "      <td>3</td>\n",
       "      <td>88</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>38</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>57</td>\n",
       "      <td>9</td>\n",
       "      <td>62</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>13</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>97</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>12</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>44</td>\n",
       "      <td>54</td>\n",
       "      <td>49</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>98</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>22</td>\n",
       "      <td>47</td>\n",
       "      <td>27</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>14</td>\n",
       "      <td>52</td>\n",
       "      <td>74</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
       "      <td>36</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>82</td>\n",
       "      <td>47</td>\n",
       "      <td>66</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    f1  f2  f3  label\n",
       "0   49  74  82     37\n",
       "1   53  93  73      5\n",
       "2   37  56  35     15\n",
       "3   40  42  71     37\n",
       "4   46  71  91     97\n",
       "5   36  43  27     66\n",
       "6   57   3  55     70\n",
       "7   41  67  72     88\n",
       "8   67  75  69     92\n",
       "9   20  15  57      9\n",
       "10  69  97  91     51\n",
       "11  54  45  38     15\n",
       "12  42  61   8      3\n",
       "13  88  72  41     24\n",
       "14  93  28  95     43\n",
       "15  48  69  42     66\n",
       "16  82  66  88     78\n",
       "17  14  53  46     14\n",
       "18  27  68  21     38\n",
       "19  42  30  88     79\n",
       "20  56  65  93     37\n",
       "21  87  66  16     60\n",
       "22  25  99  30     41\n",
       "23  72  45  26     87\n",
       "24  65  60  89     70\n",
       "25  33  59  28     16\n",
       "26  49  95  41     11\n",
       "27  86  15  95     91\n",
       "28  19  69   1     77\n",
       "29  63  87  28     70\n",
       "30   5   3  12     62\n",
       "31  26  96  61     87\n",
       "32  74  99  97     64\n",
       "33  95   3  88     59\n",
       "34  38  29  31     88\n",
       "35  57   9  62     31\n",
       "36  13  35  47      2\n",
       "37  17  50  22     30\n",
       "38  36  22  97     51\n",
       "39  38  28   4      0\n",
       "40  12  90   2      7\n",
       "41  44  54  49     76\n",
       "42  98   3  32     91\n",
       "43  43   7  42     66\n",
       "44   9   5  26     66\n",
       "45  22  47  27     46\n",
       "46  14  52  74     54\n",
       "47   3  94  36     63\n",
       "48  41  18  37     45\n",
       "49  82  47  66     97"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=mydata, columns=[\"f1\",\"f2\",\"f3\",\"label\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[\"f1\",\"f2\",\"f3\"]]\n",
    "Y = df[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train,x_test, y_train,y_test = train_test_split(X,Y,test_size=0.3, random_state=42)\n",
    "# random_state means set_seed in this library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 3)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 3)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
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 "nbformat_minor": 2
}